{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "figures.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "1CG1vSpJBxGS"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# We report results on 2, 5, 10, 25, 50, and 100 permutedMNIST tasks\n",
        "num_tasks = [2, 5, 10, 25, 50, 100]\n",
        "x_labels = [None, None, \"10\", None, None, \"100\"]\n",
        "\n",
        "# Results with an Active Dendrites Network (All numbers are averaged over 8 independent\n",
        "# trials in all figures)\n",
        "dendrites = [0.9747437313199043, 0.9636299759149551, 0.9455087259411812,\n",
        "             0.9209470000000001, 0.8704790000000002, 0.8142061159014702]"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "xd-nRhtWgOc3",
        "outputId": "5d715bc4-b140-42c5-be2c-9b9cdcb5ff07"
      },
      "source": [
        "\"\"\"\n",
        "Accuracy curves for SI, XdG, and Active Dendrites Networks on permutedMNIST.\n",
        "\"\"\"\n",
        "\n",
        "# Synaptic Intelligence\n",
        "si = [0.9800, 0.9790, 0.9710, 0.9460, 0.9050, 0.8250]\n",
        "\n",
        "# XdG-related models\n",
        "xdg = [0.9800, 0.9780, 0.9720, 0.9030, 0.7900, 0.6200]\n",
        "si_xdg = [0.9850, 0.982, 0.9795, 0.9750, 0.9620, 0.9550]\n",
        "\n",
        "# Active Dendrites Networks\n",
        "si_dendrites = [0.9765124999999999, 0.9746999999999999, 0.972305, 0.9645640000000001,\n",
        "                0.944486, 0.9159337431192398]\n",
        "\n",
        "fig, ax = plt.subplots()\n",
        "ax.plot(num_tasks, si, \"-o\", c=\"violet\", label=\"SI\")\n",
        "ax.plot(num_tasks, xdg, \"-o\", c=\"darkorange\", label=\"XdG\")\n",
        "ax.plot(num_tasks, si_xdg, \"-o\", c=\"gold\", label=\"SI + XdG\")\n",
        "ax.plot(num_tasks, dendrites, \"-s\", c=\"navy\", label=\"Active Dendrites Network\")\n",
        "ax.plot(num_tasks, si_dendrites, \"-s\", c=\"dodgerblue\",\n",
        "        label=\"SI + Active Dendrites Network\")\n",
        "\n",
        "ax.set_ylabel(\"Test accuracy\")\n",
        "ax.set_xlabel(\"Number of tasks (log scale)\")\n",
        "ax.set_xscale(\"log\")\n",
        "ax.set_xticks(num_tasks)\n",
        "ax.set_xticklabels(x_labels)\n",
        "ax.legend(loc=\"lower left\")\n",
        "\n",
        "ax.set_ylim([0.60, 1.0])"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.6, 1.0)"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "CMKwyDIdgs7H",
        "outputId": "86fe8ac2-6848-48de-b5c3-496af05ff8c6"
      },
      "source": [
        "\"\"\"\n",
        "Accuracy curves for Active Dendrites Networks where the prototype context vector is\n",
        "given while training vs inferred via an online clustering algorithm. (Both methods infer\n",
        "the prototype at test time.)\n",
        "\"\"\"\n",
        "\n",
        "prototype_given_while_training = dendrites\n",
        "prototype_inferred_while_training = [0.9755312500000001, 0.9600425, 0.9431962500000001,\n",
        "                                     0.8864204999999998, 0.8534172499999999,\n",
        "                                     0.769117996491909]\n",
        "\n",
        "fig, ax = plt.subplots()\n",
        "x = [2, 5, 10, 25, 50, 100]\n",
        "ax.plot(num_tasks, prototype_given_while_training, \"-s\", c=\"navy\",\n",
        "        label=\"Active Dendrites Network\\n(Prototype given while training)\")\n",
        "ax.plot(num_tasks, prototype_inferred_while_training, \"-d\", c=\"chocolate\", markersize=8,\n",
        "        label=\"Active Dendrites Network\\n(Prototype inferred while training)\")\n",
        "\n",
        "ax.set_ylabel(\"Test accuracy\")\n",
        "ax.set_xlabel(\"Number of tasks (log scale)\")\n",
        "ax.set_xscale(\"log\")\n",
        "ax.set_xticks(num_tasks)\n",
        "ax.set_xticklabels(x_labels)\n",
        "ax.legend(loc=\"lower left\")\n",
        "\n",
        "ax.set_ylim([0.60, 1.0])"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.6, 1.0)"
            ]
          },
          "metadata": {},
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "9RVSEf05gdh1",
        "outputId": "8ff367a8-31b2-40b1-ea8c-cd21699a2eb7"
      },
      "source": [
        "\"\"\"\n",
        "Accuracy curves when comparing with MLPs in a continual learning scenario.\n",
        "\"\"\"\n",
        "\n",
        "# Active Dendrites Network that has 10 dendritic segments per neuron regardless of the\n",
        "# number of permutedMNIST tasks to learn in sequence\n",
        "dendrites_10_segments = [0.9752812499999999, 0.9646975, 0.9455087259411812,\n",
        "                         0.9131079999999999, 0.8628207499999999, 0.7847466249999999]\n",
        "\n",
        "# Both 3- and 10-layer MLPs; results are only reported for 10 and 100 tasks\n",
        "mlp_3_layers = [None, None, 0.8203199999999999, None, None, 0.483134]\n",
        "mlp_10_layers = [None, None, 0.71843, None, None, 0.28608737500000003]\n",
        "\n",
        "fig, ax = plt.subplots()\n",
        "ax.plot(num_tasks, dendrites, \"-s\", c=\"navy\",\n",
        "        label=\"Active Dendrites Network\\n(Num. segments = Num. tasks)\")\n",
        "ax.plot(num_tasks, dendrites_10_segments, '--D', c=\"r\",\n",
        "        label=\"Active Dendrites Network\\n(Num. segments = 10)\")\n",
        "\n",
        "ax.plot(num_tasks, mlp_3_layers, \"-X\", markersize=10, c=\"firebrick\",\n",
        "        label=\"3-Layer MLP\")\n",
        "ax.plot(num_tasks, mlp_10_layers, \"-X\", markersize=10, c=\"forestgreen\",\n",
        "        label=\"10-Layer MLP\")\n",
        "\n",
        "ax.set_ylabel(\"Test accuracy\")\n",
        "ax.set_xlabel(\"Number of tasks (log scale)\")\n",
        "ax.set_xscale(\"log\")\n",
        "ax.set_xticks(num_tasks)\n",
        "ax.set_xticklabels(x_labels)\n",
        "ax.legend(loc=\"lower left\")\n",
        "\n",
        "ax.set_ylim([0.20, 1.0])"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.2, 1.0)"
            ]
          },
          "metadata": {},
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "12-W4VEPhbq1",
        "outputId": "444342ed-a1f8-4188-c712-06f2026f6764"
      },
      "source": [
        "\"\"\"\n",
        "Accuracy curves when comparing an Active Dendrites Network to 1) a network with active\n",
        "dendrites but ReLU instead of kWTA (i.e., no sparse representations) and 2) an MLP with\n",
        "kWTA.\n",
        "\"\"\"\n",
        "\n",
        "dendrites_only = [0.96759375, 0.8911, 0.828535, 0.7615365, 0.68327675, 0.615350125]\n",
        "sparsity_only = [0.96568125, 0.938915, 0.91333, 0.8459225, 0.75559925, 0.65613125]\n",
        "\n",
        "fig, ax = plt.subplots()\n",
        "x = [2, 5, 10, 25, 50, 100]\n",
        "ax.plot(x, dendrites_only, \"-^\", markersize=9, label=\"Active Dendrites only\",\n",
        "        color=\"forestgreen\")\n",
        "ax.plot(x, sparsity_only, \"-o\", markersize=9, label=\"Sparse Representations only\",\n",
        "        color=\"orangered\")\n",
        "ax.plot(x, dendrites, \"-s\",\n",
        "        label=\"Active Dendrites Network\\n(Active Denrites + Sparse Representations)\",\n",
        "        color=\"navy\")\n",
        "\n",
        "ax.set_ylabel(\"Test accuracy\")\n",
        "ax.set_xlabel(\"Number of tasks (log scale)\")\n",
        "ax.set_xscale(\"log\")\n",
        "ax.set_xticks(num_tasks)\n",
        "ax.set_xticklabels(x_labels)\n",
        "ax.legend(loc=\"lower left\")\n",
        "\n",
        "ax.set_ylim([0.50, 1.0])"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.5, 1.0)"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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30FoJzd7T1iAoKQoNjWL9+st4eHhy6tR9bG0tcHevyahRdahTp5RqJZhQdk4EpYD5QEXgENADqCmlfJ7SfVWL4PWkNvV09pHZ/HryV9pXac+8zvNMHW72EBQIe1dqXUf3vMG+ELQbCh1HQuk3TR1dtnf27EMWLTrD6tWXCAmJonbtkowa5Ya7uxP581uZOrw8J9t2DSW6Pj9wTUpZxtB9VSJIv8RTTzu91YndPrsJjQrFxsKGDe4bcCzuaOowsw8p4fx+rdvo6BZtrMGtLbz7AdTvmOd2aEurFy/CWb36EgsXnuHy5ScUKGDNgAHOjBzphpOTmq2VVUyVCCzQBotbAffRBov7Sim94l1TBAiQUuqFEN8BOinll4buqxJBxomderr24lqi9dpqXIHAqYQTm/ttVs345Dx7AH//ppWx8L8PRcq8moJaWC3qM0RKyfHj9/DwOMOGDV5EROho3Lgso0bVoWfPGtjYqISamUw5fbQDMAdt+ugyKeV3QojpwBkp5baY7qPv0ZaBHgLGSCkjDN1TJYKMJaXk7cVv8yjoUdwxc2HOtFbT6Ofaz4SRZXO6aDixA7b/Cmf3aq2Cxt20sQSX5moKair8/UNZufI8Hh6e3LgRQOHCtgwZ4sqIEW5UqVLY1OHlSmpBmZKiw36H+eCvDwiNSjrnvtNbnRjXaByVClUyQWQ5yH0f2LkIdi/TxhXKvgWdRkGbQXm2sJ2x9HrJ/v238PDwZOvWa0RH62nduhKjRrnRuXM1LC3Vyu+MohKBkqLea3tz5n7Sn6eFmQVI0KOnc/XOfNjwQyo6VDRBhDlIRBgc3KCNJVw7Cda22hTUTqO13dcUgx4+DGLp0nMsXuzJ3bsvKVkyP8OG1WbYsNqUK6fWdKSXSgRKsq77X6fbqm6ERydfjiGfVT66O3bnz0t/EqWLokuNLnzY8EPKFyyfxZHmQD5ntYTw3xptQVu1etqahGa9tQShpEin0/P33zfw8DjDrl0+CCHo2LEKo0bVoV27ypibq6J3r0MlAiVZ43eOZ8e1HeikLtnzNhY2DKg1gPfrvM/iU4tZfWE10bpoujl2Y0yDMZQrWC6LI86Bgp/Dv39oSeHOVW2rzDaDta6jMlVNHV225+f3nCVLPFm69ByPH4dQoULBuHIWxYvnN3V4OYpKBEqyOqzogLe/t8FrHIs5sm3gNgCeBD9h0alFrLmwBr3U092xO2MajKHMGwZn/CqgTUG9eFBLCEc2a4PNtVprrYSGndUU1FRERur4669reHh48t9/t7CwMKN79+qMGuVG8+YV1Aw3I6hEoGSoR0GP8DjlwfqL69FLPT1r9mRMgzFqRbKxAh69moL69C4ULvVqCmqR0qaOLtvz9vZn0SJPVqw4T2BgONWqFWbkSDdmzDjCkydJJz3E7rOQ16lEoGSKh0EP8TjpwYZLG5BS0supF6Prj1YJwVi6aDi1SytnceYfrUBeoy7aQjXXlmoKairCwqL4888reHic4fhxg0WLkXJaFkWVfalEoGSqBy8fsPDkQv689CdCCN5zeo9R9UepXdPS4sHNV1NQXz7Txg86joK2g7VxBcWgCxce4eq6KMXzKhGoRKBkkfsv7rPg5AI2Xd6EmTCjj3MfRtUfRfH8qoyA0SLD4dBG2PErXDmuFblr4a5NQa1W19TRZWuG9mIOCpqS5+sbqUSgZKm7z+/y68lf2XR5ExbmFrg7uzOy3kiK5S9m6tBylpsXtMHlfau0rTeruGkJoYU72NiZOrpsx1AicHCwYfToOnz4YT1KlrTPwqiyD5UIFJO48/wOC04sYIvXFizMLejn0o+R9UZSJF8RU4eWs4S8hH1/aGMJt7201cptBmldR+XeMnV02YahRNC9e3W2bLmKpaU5/fs7MWFCI2rUKJqF0ZmeSgSKSfkF+rHgxAK2XtmKlbkV/V37M7zucJUQ0kpKuHxEq290ZBNER4FLC21wuVEXbcvNPKxEiZk8fhyS5HjsrKEbNwKYPfs4y5efJywsmo4dq/Dpp41o1qx8nph+qhKBki3cCrjF/BPz2XZ1G9bm1gyoNYDhdYdTyK6QqUPLeQIfwz/LYNcieHwbCpXUpp92GAFF1boOQ/z9Q1m48DTz5p3i6dNQ3NxK8umnjejZswYWFrl31bJKBEq24hvgy7zj89h+dTu2lrYMrDWQYXWH4WCrZsekmU4Hp//WxhJO/w3CDBq8q40l1G4NZin8Yku8jWeTnnludlJYWBSrVl1k1qzjeHs/o3z5N/j44wa8/34t7O2tTR1ehlOJQMmWfPx9mHd8Hru8d2FnacfA2gMZVmcYBW1Vxc7X8vCW1kL4Zym88IdSb2qlLNoOhgIxpZ2lhJVfwsaZ2rqF8FBt4Fmvg56fwqDpeW79gl4v2bHjOjNnHuPw4TsULGjDqFFujB1bn1Klcs/AskoESrZ23f86847NY9f1XeS3ys/g2oMZWmdogj2VlTSIjNDGEHYs1MYULK21YnfvjoaTO2HTz1ohvMSs7aDHeBj8TdbHnE2cPHmPWbOOs2nTVczNBf36OTNhQkNq1sz5M95UIlByBO+n3sw9Npd/fP7B3tqeIbWHMMRtCAVsCpg6tJzr1iVtttG+PyAsWPu0b+j/vJUNrHuY5/dR8PUNZPbs4yxbdp7Q0Cjat3+TiRMb0aJFzq1rpBKBkqNcfXKVucfnssdnDwWsCzC0zlAG1x6MvXXuaaZnudAgmDsa9q8xnAhs8sGo2dBheNbFlo09exaKh8cZ5s07xePHIdSqVYJPP21Er141ctymOYYSQe4dIldyrOrFqrOwy0K2DdhGvTL1mHN0Ds2WNGPBiQUERwabOrycyc4eylTTNoU1JCJUG0BWAChc2I7/+7+m+Pl9zG+/vUtYWDT9+m2mcuW5/PzzcYKCDO6sm2OoRKBkW47FHVnUbRFb+2/FrZQbPx/5mWaLm7Hw5EKVEF5HoRKpr0i2ttOuUxKwsbHg/fdr4+X1Adu3u1OpkgMTJuyhbNnZTJq0l/v3X5o6xHRRXUNKjnHx4UV+OfYLB24dwMHWgeF1htO/Vn/yWeUzdWg5Q1AguJfS6hmlxNIa1j/K82MExjh9+j6zZh3nzz+vYGYm6NvXiQkTGuLsnD1ra6muISVXcC7pzNIeS9nUbxNOxZ348fCPNF/SnCWnlxAWFWbq8LI/ewdtiqi1gVaBLlrbXjOHfUA0hbp1S7NuXU9u3BjLBx/UYdOmK7i4eNC+/Sr+/deXnPQhW7UIlBzr7IOz/HL0F47cPkJhu8KMrDeSvi59sbV8tSewf4g/Y7ePZVaHWWqfBEi6jiAiVEsMep22CO32FfDcDY26wvilUECt+jZWQEAYixadYe7cUzx6FIyLS3E+/bQRvXs7ZouBZTVrSMnVztw/wy9Hf+HYnWMUzVeUkfVG4u7sjo2lDbOPzObXk7/Svkp75nWeZ+pQs4/EK4ub9tK6g/R62DwHlk2GgsVh8mpwbmrqaHOUiIho1qy5xMyZx7ly5SllyhRg3Lj6DB9emzfesDFZXCoRKHnCqbun+OXYL5y4e4Ji+YoxrM4w5h6fS3BkMDYWNmxw34BjcUdTh5kzXPeE//WBR77Q9wvoN1Xtq5xGer3kn39uMHPmMfbv98Pe3oqRI9346KP6lC2b9YslVSJQ8pQTd07wy7FfOHXvVNwxgcCphBOb+23OsQuCslxoEMz/EP79HWo20VoHxcqaOqocydPzAbNmHWfDBi+EEPTpU5MJExri6pp1M7RUIlDyHL1eT0OPhviH+scdMxfmfNz4Y0bXH62SQVr8uwrmjdbKXH+yFN7uZuqIcqzbt58zZ84Jliw5S0hIFK1bV2LixEa0aVMp0/9NqllDSp5z9M5RQqMS1tPRSR2zjsyizbI2LPdczovwFyaKLodp3R9+PQclKsH07jD3A4hQs7ReR/nyBZk9uz13737CjBmt8PJ6Qrt2q3Bx8eD33y8QGakzSVyptgiEEO8CO6WU+qwJyTDVIlCM0Xttb87cT/rvxMrcimL5inHv5T2sLazpVK0T/Vz74VzCWbUSUhMVCcv/T5txVKEmfL4OKqgxl/SIjNSxdq02sHz58hNKlbJn3Lj6jBjhRsGCGTuwnK6uISHEKqAhsAlYJqW8lqHRpZFKBEpqrvtfp9uqboRHJ79wyt7Knt+6/8bWq1v568pfhEaF4ljMkb6ufXn3rXfVArXUnP4HfhoEYUEwao5Wl0gl0XSRUrJ7901mzjzGvn23sLe3Yvjw2owb14B69ZYY3HnNWOkeIxBCFADcgSFo1UqWA2ullEFGR5FBVCJQUjN+53h2XNuBTibfzLaxsGFArQFMbjaZoIggtl3dxurzq/H29ya/VX661eiGu4s71YpWy+LIc5CAR/DjQDi7F97uAZ8syXMb22SWc+ceMmvWcdatuwyATpfy72gppxl933SPEUgpXwIbgXVASaAbcFYIMdboKBQli1x7ei3FJAAQHh3OsdvHALC3tqefaz92DtrJBvcNtH6zNesvrafDyg70XtubbVe3ERGdOwqLZahCJeB//8CwH+H4XzDaFbyOmjqqXKFWrZKsWtUdX99xjBtXP0te05iuoc5oLYE3gd+BlVLKJ0IIO+CKlLJCpkcZj2oRKJktIDSATV6bWHNhDXee36GQbSF61uyJu4s75QqWM3V42c+1U/C9Ozz2g/5fgfvnYG76lbS5hRBfp3guo1oExiSClcBSKeWhZM61klLuMzqSDKASgZJV9FLPsdvHWH1hNftu7EMndTSp0IR+rv1oUakFFmZqgVWckJev9jtwbgaTVkHRMqaOKlfILomgIvBQShke89gWKC6l9DM6ggykEoFiCo+CHrHh0gbWX1zPo+BHlLAvQR+nPrzn/B7F82fPapNZTkrY+zvMH6NVMf10OTTsbOqocrysSATGjBH8CcSfOqqLOWbMC7cXQngLIW4IISYnc76cEGK/EOKcEOKiEKKDMfdVlKxWwr4EHzX6iIMjDrKwy0KqFK7CnGNzaLKoCWP+GsPR20fRZ48Z1qYjBLQdBL+eheLlYVoXWDDWcNlrJVXFiyc/iy2l46/DmBbBeSmla6JjF6SULqk8zxy4DrQB7gGnAXcp5ZV41ywGzkkpFwohagC7UhtzUC0CJbvwC/Rj3cV1bLy8kcCwQCo4VMDd2Z0eNXvgYJvHZ9BERsCyKbB5NlRy1tYclKtu6qjytPS2CJ7GDBjH3qwL4G/g+lj1gBtSSl8pZSTajKMuia6RQOzO5G8AD4y4r6JkCxUcKjC52WSOjjzKrA6zKGxXmO8Pfk8jj0Z8uutTzj44m6Nq0mcoK2sY9TN8uxOePYAxdeDvpWqfg2zKmBZBZWA1UAoQwF1goJTyRirP6wm0l1IOi3k8AKgvpfww3jUlgT2AA5APaC2l9EzmXiOAEQDlypVzu337ttFvUFGy0rWn11hzfg1br2wlJCqE6kWr09e1L52rdya/VX5Th2cazx7CjwPg3D5o1hvGeagd0EwgQ4rOCSHyA0gpjdos1shEMD4mhllCiIbAUqCmoXIWqmtIyQmCI4PZdnUba86v4erTq+S3yk+X6l3o59ovby5U0+thw4+wYioULQtT1kCNhqaOKk/JiJXFHQFHIK74hZRyeirPaQh8JaVsF/N4Sszzvo93jRdasrgb89gXaCClfJLSfVUiUHISKSXnH55n9fnV7PTeSaQuktqlatPPtR/vVH0HawtrU4eYta6egP+5w9O7MOgbeO8zteYgi6R3+qgHYAe0AH4DegKnpJTvp/I8C7TB4lbAfbTB4r5SSq941/wNrJdSrhBCVAf2AaWlgaBUIlByqudhz+MWqvkF+uFg60APxx64u7hTwaGCqcPLOiEvYM5IOLgeXFvCpD+gsNpGNLOlNxFclFI6x/uaH/hbStnEiBfuAMwBzNEK1n0nhJgOnJFSbouZKbQEyI82cPyZlHKPoXuqRKDkdHqp5/id46w5v4a9N/aikzreLv82fV370qpyq7yxUE1K2L1cm15qYwefroD6HU0dVa6W3kRwSkpZTwhxAugOPAO8pJRvZnyoqVOJQMlNHgc/ZsOlDay7uI5HQY8onr84vZ1609u5NyXss273KpO5c1XrKvK9AN3Gwfs/aB+H2bEAACAASURBVDOOlAyX3kTwBTAPrYtnAdon9yVSyi8zOlBjqESg5EbR+mgO+B5g9fnVHPY7jJkwo2XllvRz7Ufj8o0xE7l4D6nIcFjyGfw1D96spa05KFPV1FHlOq+dCIQQZmiDt8diHlsDNlJKk23tpBKBktvdeX6HdRfX8eelPwkIC6BcwXK4O7vTs2ZPCtkVMnV4mef4dpg1REsMH86HNoPUPgcZKL0tgnNSylqZEtlrUIlAySsioiPY7bObNRfWcPreaazMrXin6jv0de2LWym33Lmjmv99+KE/XDgALdzhIw/IVyDVpympS28imAkcBzYbms2TVVQiUPKi6/7XWXNhDVu8thAcGUy1ItXo69KXLjW6YG9tb+rwMpZOB+tnwO/TtJpFU9bCW/VMHVWOl95EEIS26jcaCEdbXSyllCZJ0yoRKHlZSGQIO67tYPWF1Xg99iKfZT461+hMX5e+1ChWw9ThZSyvY9o+B88ewJDvoOenYJaLx0oyWYasLM4uVCJQFG2h2sVHF1lzYQ3br20nIjqCWiVr0de1Lx2qdsDGMmM3PjeZoECYMwIOb4TabeCz37Xd0ZQ0S2+LoGlyx5PbqCYrqESgKAm9CH/BZq/NrLmwBt8AXwraFNQWqrm6U9GhoqnDSz8p4e/fYOE4sMmvJYO67U0dVY6T3kSwPd5DG7Sqop5SypYZF6LxVCJQlORJKTlx9wRrLqxhj88eovXRNCrXiH6u/WhVuRWW5pYJrvcP8Wfs9rHM6jCLUgVywMre21fgf33g1iXoOQGG/A8srUwdVY6RoV1DQoiywBwpZY+MCC6tVCJQlNQ9DXmqLVS7sI4HQQ8olq8Y7zm9R2/n3nG/9Gcfmc2vJ3+lfZX2zOs8z8QRGykiDBZ/Ctt/hSpu8PlaKF3F1FHlCBmdCATaymKTjEypRKAoxtPpdRy8dZA1F9ZwwPcAQghaVmpJL6deTNg1geDIYGwsbNjgvgHH4o6mDtd4R7fCz0MhOgrG/gqtB2jHgwK18YSAR9pYQpOeYJ/HNwmKkd6uoXloq4lB28jGFfCTUvbP0CiNpBKBoryeey/use7iOjZc2sCz0GcIBBKJQOBUwonN/TbnrLUJT+5qaw4uHYJW/aFQSW11spk5hIdqNYz0Om220aDpeX5xWnoTwaB4D6PRksDRDIwvTVQiUJT0iYiOoPGixgSGBcYds7GwYf6782lRuYUJI3sNOh2s+Rb++IqYme1Jr7G2gx7jYfA3WRxc9pLerSo3AquklCullKuBE0IIuwyNUFGULHPq3ikioiMSHAuPDueTnZ8QGhVqoqhek7k5dP0ILKxINgkARITCxpkQ/DxLQ8tJjEkE+wDbeI9tgX8zJxxFUTLb/OPzk/2FHxQZRIslLThzL4e1uA9vBAtLw9eYmcOhP7MmnhzImERgE397ypjvVYtAUXKg6/7Xufz4cornn4U+o/e63vzfnv/jZfjLLIwsHQIeaWMChkSEatcpyTImEYQIIWrHPhBCuAFhmReSoiiZxeOkB1G6qBTPW1tY41TciQ2XNtB2eVt2ee8i21cfKFRCGxg2xNpOrUg2wJhE8DHwpxDisBDiCLAe+DCV5yiKkg1de3oNndSleD48Ohy91LOl/xaK5y/O2O1jGb5lOPdf3M/CKNOoSU9tdpAhURHwtkmWPuUIxm5ebwlUi3noLaVM+SNFJlOzhhQla0Tro/nj3B/8fORnAD5p/AkDaw/MnltprvgCNv2sdQElZmauJYpGXbXyFHa5rFqrkdI1a0gIMQbIJ6W8LKW8DOQXQnyQ0UEqipK9WJhZMMRtCLuH7KZ+2fp8d+A7eqzugddjL1OHltSg6doUUSsbsMmnrRmwyac97jMFRs2GE9thXAO472PqaLMdY9YRnJdSuiY6ZrLNalSLQFGynpSSv6//zfT/pvMs9BlD3IbwcaOPsbPKZvNGEq8sbtoL8hfUzp3fD9/2Al00TFkD9TqYNtYslt4FZZcA59hNaYQQ5sBFKaVJ1qOrRKAopvMy/CU/Hv6RtRfWUrpAab5u/TUtKuWgRWiP/ODrbuB7AQZ9A+6f55kVx+ldUPYPsF4I0UoI0QpYG3NMUZQ8poBNAb5t8y3r+6zH1sKWYZuHMW7HOPxD/E0dmnFKVIDZR6G5O6yYCt/0grDgVJ+W2xnTIjADRgKtYg7tBX6T0sDUg0ykWgSKkj1EREew+PRifj3xKzYWNkxuNpleTr0wEzlgFzEpYdNs+G0ilK0OX22F0m+aOqpMpXYoUxQl0/gG+DJ1z1RO3jtJ3TJ1+bbNt7xZOIf8Uj37L3zXG6Re2xs5F294k95ZQ1WEEBuFEFeEEL6xfzI+TEVRcqJKhSqxuvdqZrSbwXX/67z7+7v8cvSXJPWMsqXarWH+GShWHqZ2gHUztNZCHmNMG245sBCt8mgL4HdgVWYGpShKziKEoJdTL/YM2cM7Vd9h7vG5vPv7u5y6e8rUoaWuZEVt3KDpe7BsitZCyGPjBsYkAlsp5T60bqTbUsqvgI6ZG5aiKDlRkXxF+LnjzyzvsZxIXSTu692ZsnsKL8JfmDo0w2zzabudDfsRjmyCjxvBw7zT8WFMIoiIGTD2EUJ8KIToBuTP5LgURcnBmlZsyt+D/2ZE3RFsuryJNsvasP3a9uxdt0gIeG8ifPs3+N+DD+vAmT2mjipLGJMIxqFVG/0IcAP6A4MMPkNRlDzP1tKWSc0msXXAVkoXKM3HOz7m/c3vc+/FPVOHZlidtjDvNBQpA1PfgQ0/5fpxAzVrSFGUTKfT6/jj/B/8fPhn9FLPuMbjGOI2JHvWLYoVFgKzhmj7GDTvA5/8pnUh5VDpXVCmKIqSLuZm5gyuPZh/hvxD4/KNmXFwBt1Xdefio4umDi1ltvng/9bD+zPg4Hr4pDE8vGXqqDKFSgSKomSZUgVK4dHVg187/8rTkKf0WN2Db/d/S0hkiKlDS54Q0HsSfLsLntzWxg3O5r4NGo1ZR9DYmGOKoijGEELQrmo79gzdg7uzOys8V9B+eXv+u/mfqUNLWd322rhBoZLweTvYOCtXjRsY0yKYZ+QxRVEUo9lb2zO9zXTWu68nn1U+hm8ZzthtY3kS/MTUoSWv9Jvwy3FtX4PFn8KM/qlvkZlDpDhYLIRoCDRC26FsdrxTBYBuUkqXzA8vKTVYrCi5T6Qukt9O/8a84/OwtrDms6af0ce5T/asWyQlrPteK1pXyUWrU1S8vKmjStXrDhZboa0XsADs4/15CfQ08oXbCyG8hRA3hBCTkzk/WwhxPubPdSHEc2PuqyhK7mJlbsUHDT5g1+BdOBZz5Iu9X9BnXR+u+183dWhJCaGVr/5mBzy6pY0bnN9v6qjSxZjqo+WllLdjvjcD8kspX6Z6Y23fgutAG+AecBpwl1JeSeH6sUAtKeVQQ/dVLQJFyd2klGz22sz/DvyPkMgQRtQbwZgGY7C2sDZ1aEnd94FpXeDedRgxE7qNy7b7G6R3+uj3QogCQoh8wGXgihBiohHPqwfckFL6SikjgXVAFwPXu6PtdaAoSh4mhKBHzR7sGbqHjm91ZMGJBXRY2YETd06YOrSkSleBuSehwbvg8Qn8OBAiwkwdVZoZkwhqxLQAugJ/AxWBAUY8rzRwN97jezHHkhBClI+5b7LTBoQQI4QQZ4QQZ54+fWrESyuKktMVtivMrA6zWNlzJXq9nn4b+jHpn0kEhgWaOrSE7Ozhy00wcDrsWwWfvA1P7pg6qjQxJhFYCiEs0RLBNillFJDR86b6ABtT2uxGSrlYSllHSlmnaNGiGfzSiqJkZ29XeJtdg3cxqt4otnhtoe2ytvx15a/sVbfIzAz6fwFfb4MHN2BMHbh40NRRGc2YRLAI8APyAYdiPr2nOkYA3AfKxntcJuZYcvqguoUURUmBraUtE5tOZNvAbZQrWI7xu8YzZNMQ7jzPZp+8G74L805BgcLwWSvYOi9HrDdINRFIKedKKUtLKTtIzW20fQlScxqoIoSoKISwQvtlvy3xRUKItwAH4HgaY1cUJY95q+hbbHDfwLSW0zj34BzvrHiHRacWEaWLirvGP8Qf93XuPHj5wDRBlq2mjRvU7wi/fgQzh0BkuGliMZIxK4uLCyGWCiH+jnlcAyOqj0opo4EPgd3AVWCDlNJLCDFdCNE53qV9gHUyW7XzFEXJrszNzBlYeyD/DPmHJhWa8OOhH+m6qisXHl4A4I9zf3Dm/hm+P/C96YLMVwCmbYEBX8HelTC+CTy5m+rTTMWY6aN/o+1S9n9SShchhAVwTkrplBUBJqamjyqKEt9un918ve9rngQ/oZ9rP7Zc2UJIZAg2FjZscN+AY3FH0wZ47C/4cQBY2cLUP8G5qUnCeK3pozG/8AGKSCk3AHqI+6Sf7KCuoihKVmtXpR27h+ymv2t/Vp1fRWikVvYhIjqCqXunmn5QuVEXrasof0GY1Aq2Lch24waGuoZiNxsNEUIUJmamkBCiAZDN951TFCUvsbe2Z1qraRS1K4qMmdQokXg/9eaA7wHTBgdQrro2iFynHcz/EH4elq3GDQwlgtjlcePRBnkrCyGOom1ePzazA1MURUmLI7ePEBKVsJx1hC6CMdvHcO3JNRNFFU++N7TppX2nwu5lMKEZ+Kc0kTJrGUoERYUQ44HmwBbgR7QFZUuA1pkfmqIoivHmH59PaFTSaqAR0RF0/L0jn+/53PSVTc3MYPA32gK0O1dgjBt4HTVtTBhOBOZoRefs0dYQWMQcs4s5piiKki1c97/O5ceXUzxvaWbJpkubaPlbS+YcnUNwZHAWRpeMt7tr4wa29jCxBezwMOm4gaENQx9KKadnWSSKoiivyeOkR4K1BImZm5nTpXoXgiKDmHd8HmsvrGVco3H0cuqFpbllFkYaT/kaMP80fN8X5o4GH08YMx+ssr64njFjBIqiKNnatafX0CVfoQaA8OhwLj++zLx357Gp3yYqFqrIF/9+QYeVHdjjs8d0M4vyF4Tp27Wy1n//BhObw7OsXwhnaGOaQlLKgCyOJ1VqHYGiKOklpWTfzX38eOhHbgbcpE7pOkxuNplapWqZLqhDG2Hm4JgidpuhRsMMvb2hdQSpLijLblQiUBQlo0Tro/nz0p/MOToH/1B/3qn6DhOaTKCiQ0XTBHTrMnzVBZ7ehQ8XQIfh2vGgQDi8EQIeQaES0KQn2Duk6dYqESiKohgQEhnC0jNLWXJ6CZG6SNxd3BnbcCyF7QpnfTAvA2BGXzizGzqM0ArYbZ4NZubaHsk2dqDXQc9PYdB0ozfCUYlAURTFCE9DnjL32FzWX1yPraUtI+uNZIjbEGwtbbM2EJ1O2xN5/QwQZiD1Sa+xtoMe47XpqEZQiUBRFCUNbj67yY+Hf+TfG/9SPH9xPmn8Cd0du2NuZp51QQQFQu/iEJ3ybCisbGDdQ23QORXp3apSURQlT6lcuDKLui5iXZ91lLQvyeTdk+n0eyf2++7PuhlGhzeChZXha8zM4dCf6X4plQgURVFSULdMXTb23cj8d+cTHh3OsM3DGPDnAC49upT5Lx7wSBsTMCQiVLsunVQiUBRFMUAIwTvV3mH3kN182fJLvJ9603VVVz7Z+Qn3XtzLvBcuVEIbGDbE2k67Lp1UIlAURTGClbkVg2oP4r9h/zG6/mh2++ymzbI2/O/A/3ge9jzjX7BJT212kCF6HTTtle6XyhWDxVFRUdy7d4/w8OxT1lVRlMxhY2NDmTJlsLQ0UWmIGA+DHjLn6Bw2Xd6EvbU9HzT4gIG1BmJtkYElIlZ8AZt+1rqAElOzhhImglu3bmFvb0/hwoURRs6pVRQl55FS8uzZM4KCgqhY0USLvhLxfurND4d+4OCtg5QuUJoJb0/g3ervYiYyoMNFSlj5JWycqQ0MR4RqCUCtI0iaCK5evcpbb72lkoCi5AFSSq5du0b16tVNHUoCR28f5YeDP+D1xAvHYo5MajaJxuUbZ8zNE68sbtrLqCmj8RlKBIaqj+YoKgkoSt6QXf+vNy7fmK0DtrL96nZmHp7JwD8H0rRCUyY3m0y1otXSd3N7h1flJjKBGixWFEXJIGbCjC41uvDv+/8ypdkUzj88T8eVHZn0zyQeBj00dXgpyrOJwD/EH/d17jx4mXElX7du3YoQgmvXUt8Wb86cOYSGvhoA6tChA8+fp3/mQYUKFXBycsLJyYkaNWowderUDBtE9/Pzo2bNmsmee/DgAT179gTg/Pnz7Nq1K0NeM62++uorZs6caZLXVpRY1hbWDKs7jP3D9jO0zlC2Xd1Gq6WtmHl4JkERQaYOL4k8mwj+OPcHZ+6f4fsD32fYPdeuXcvbb7/N2rVrU702cSLYtWsXBQumrc8vJfv37+fSpUucOnUKX19fRo4cmSH3TUl0dDSlSpVi48aNgGkTgaJkJwVtC/J588/ZO3Qv7aq0Y+HJhbT8rSUrz64kUhdp6vDi5MlEEBEdwYqzK9BLPf/5/ofXY6903zM4OJgjR46wdOlS1q1bF3dcp9Px6aefUrNmTZydnZk3bx5z587lwYMHtGjRghYtWgDaJ3l/f38mT57MggUL4p4f/xPuTz/9RN26dXF2dmbatGmpxpQ/f348PDzYunUrAQEBKd7Dz8+P6tWrM3z4cBwdHWnbti1hYWEAeHp64uLigouLS4K4VqxYQefOnWnZsiWtWrWKay1ERkby5Zdfsn79elxdXVm/fj0hISEMHTqUevXqUatWLf766y8AvLy8qFevHq6urjg7O+Pj45PkPaxduxYnJydq1qzJpEmTEry3//u//8PFxYUGDRrw+PHjBM+7efMmtWvXjnvs4+OT4LGiZKUyb5RhdsfZbO2/lapFqjL9v+m0W96OXd67TLcpTjy5ZrA41jf/fcPVJ1cNXvMk5EncJtfh0eG4r3PHsZhjioNQ1YtV54uWXxi8519//UX79u2pWrUqhQsXxtPTEzc3NxYvXoyfnx/nz5/HwsKCgIAAChUqxM8//8z+/fspUqRIgvv07t2bjz/+mDFjxgCwYcMGdu/ezZ49e/Dx8eHUqVNIKencuTOHDh2iadOmBuMqUKAAFStWxMfHhxcvXiR7j3LlyuHj48PatWtZsmQJ7733Hps2baJ///4MGTKE+fPn07RpUyZOnJjg3mfPnuXixYsUKlQIPz8/AKysrJg+fTpnzpxh/vz5AHz++ee0bNmSZcuW8fz5c+rVq0fr1q3x8PBg3Lhx9OvXj8jISHS6hItnHjx4wKRJk/D09MTBwYG2bduydetWunbtSkhICA0aNOC7777js88+Y8mSJUydOjXuuZUrV+aNN97g/PnzuLq6snz5coYMGWLwZ6Uomc2phBOr3lvFwVsH+eHQD4zdPhbXkq5MbjaZumXqmiyuPNcikFJy/+V99PHKuoZFh/E8PH3982vXrqVPnz4A9OnTJ6576N9//2XkyJFYWGg5t1ChQgbvU6tWLZ48ecKDBw+4cOECDg4OlC1blj179rBnzx5q1apF7dq1uXbtWrKfoJMT+4nD0D0qVqyIq6srAG5ubvj5+fH8+XOeP38el2wGDBiQ4L5t2rRJ9f3Evu6MGTNwdXWlefPmhIeHc+fOHRo2bMj//vc/fvjhB27fvo2tbcJSv6dPn6Z58+YULVoUCwsL+vXrx6FDhwAt4XTq1ClBvIkNGzaM5cuXo9PpWL9+PX379jXq56UomUkIQfNKzdkxcAcz2s3gYdBD+qzrw8itI7n57KZJYsp1LYLUPrkf9jvMB399kKB/Ti/1BEcGs33QdizM0v4jCQgI4L///uPSpUsIIdDpdAgh+Omnn9J8L4BevXqxceNGHj16RO/evQHtl/mUKVPS3N8fFBSEn58fVatWTfEefn5+WFu/Wg1pbm4e1zVkSL58+YyKQUrJpk2bqFYt4RS66tWrU79+fXbu3EmHDh1YtGgRLVu2NOqelpaWcS04c3NzoqOjk1zTo0cPvv76a1q2bImbmxuFC5tgkxFFSYG5mTm9nHrR6a1OLPdczqJTi3hnxTv0du7NR40+omi+olkWS55rEcw/Pj+uWyi+5+HPWX9x/Wvdc+PGjQwYMIDbt2/j5+fH3bt3qVixIocPH6ZNmzYsWrQo7hdVbF+9vb09QUHJzx7o3bs369atY+PGjfTqpdURadeuHcuWLSM4OBiA+/fv8+TJE4NxBQcH88EHH9C1a1ccHBzSfI+CBQtSsGBBjhw5AsDq1auN+nkkfm/t2rVj3rx5cS2Tc+fOAeDr60ulSpX46KOP6NKlCxcvXkxwn3r16nHw4EH8/f3R6XSsXbuWZs2aGRUDaKUI2rVrx+jRo1W3kJJt2Vra8kGDD/hv2H/0de3LhksbaPlbS+Yem0tIZEjcdZkx0zFWnkoE1/2vc/nx5WTPhUaF8tOhn15ratfatWvp1q1bgmM9evRg7dq1DBs2jHLlyuHs7IyLiwtr1qwBYMSIEbRv3z5usDg+R0dHgoKCKF26NCVLlgSgbdu29O3bl4YNG+Lk5ETPnj1TTCQtWrSgZs2a1KtXj3LlyrFo0aI03yPW8uXLGTNmDK6urkYParVo0YIrV67EDRZ/8cUXREVF4ezsjKOjI198obXaNmzYQM2aNXF1deXy5csMHDgwwX1KlizJjBkzaNGiBS4uLri5udGlSxejYojVr18/zMzMaNu2bZqepyhZrbBdYb5q9RX/DPmHZhWb8cuxX2j5W0vWXFhDtD46U2Y6xso1JSaMWW4+fud4dlzbgU4mX9HPxsKGAbUGMLnZ5AyJVTG9mTNn8uLFC775xrjCXErOYOz/+Zzs7IOzzDg4A8/7nlR0qMjDoIeER4djY2HDBvcNOBZ3TNP91A5lMa49vZZiEgBtBtGx28eyMCIlM3Xr1o3ff/+dcePGmToURUmz2qVqs77PehZ2WUhQRBDh0drC0IjoCKbunZqh005z3WCxIbsGq0VOecmWLVtMHYKipIsQgjZvtuGb/161aCUSn2c+HPA9QIvKSbuWX0eeahEoiqLkNEduH0kyvT0sKowv//2SaH3S2XKvQyUCRVGUbCwzZjomlqmJQAjRXgjhLYS4IYRIdgRWCPGeEOKKEMJLCLEmM+NRFEXJSTJrpmNimZYIhBDmwALgHaAG4C6EqJHomirAFKCxlNIR+Diz4kkgKBB2LYFV32hfgwKz5GUVRVHSwuOkB1G6qBTPR+mjWHBiQYrnjZWZLYJ6wA0ppa+UMhJYBySeBD4cWCClDASQUhpeIZVeUmp7gLqXAo9P4Pdp2lf3UtrxdIzCf/fddzg6OuLs7IyrqysnT57MwMBfT/PmzalWrRouLi7UrVuX8+fPmzokoxhbvTTxddu2bWPGjBmZGVqarFixgg8//NDUYSg5WFbNdMzMWUOlgbvxHt8D6ie6piqAEOIoYA58JaX8J/GNhBAjgBEA5cqVe/2IVn6pbQQdGa8+f3jMyr1NP2tfjdwIOr7jx4+zY8cOzp49i7W1Nf7+/kRGpr/EbHR0dFyNote1evVq6tSpw/Lly5k4cSJ79+5N1/2klEgpMTPLvM8Q58+f58yZM3To0CFN13Xu3JnOnTtnWlyKktWyaqajqQeLLYAqQHPAHVgihEhSlF9KuVhKWUdKWado0desvxEUqG0AHZF00AXQjm+cCcFpLz738OFDihQpElevp0iRIpQqVQrQykt/9tlnODk5Ua9ePW7cuAHA9u3bqV+/PrVq1aJ169ZxZZS/+uorBgwYQOPGjRkwYECKpZpXrVoVd3zkyJFJKncm1rBhQ+7fvw+QYlnoFStW0KVLF5o3b06VKlX4+uuvAa0WUbVq1Rg4cCA1a9bk7t27yZazDgkJoWPHjri4uFCzZk3Wr9cGsjw9PWnWrBlubm60a9eOhw+1nZqaN2/OpEmTqFevHlWrVuXw4cPJlrE+deoUDRs2pFatWjRq1Ahvb+9kr4v/CdzPz4+WLVvi7OxMq1atuHPnDgCDBw/mo48+olGjRlSqVCluD4WHDx/StGlTXF1dqVmzJocPH07yM9y3bx+1atXCycmJoUOHEhEREfd3PG3aNGrXro2Tk1OSjYliN1qPitKa+C9fvkzwWFFMLvYTXkb/ARoCu+M9ngJMSXSNBzAk3uN9QF1D93Vzc5OJXbly5dWDX8dJOaFZ0j+Dq0rZzkzKNqT8p52Zdl3i5/46LslrxhcUFCRdXFxklSpV5OjRo+WBAwfizpUvX15+++23UkopV65cKTt27CillDIgIEDq9XoppZRLliyR48ePl1JKOW3aNFm7dm0ZGhoqpZTyww8/lKtWrZJSShkRESFDQ0PllStXZKdOnWRkZKSUUsrRo0fLlStXJomrWbNm8vTp01JKKWfPni2nTJkipZRyypQp8o8//pBSShkYGCirVKkig4OD5fLly2WJEiWkv7+/DA0NlY6OjvL06dPy1q1bUgghjx8/LqWUcvfu3XL48OFSr9dLnU4nO3bsKA8ePCg3btwohw0bFvf6z58/l5GRkbJhw4byyZMnUkop161bJ4cMGRIXX+z73rlzp2zVqpWUUsrly5fLMWPGxN3nxYsXMioqSkop5d69e2X37t2TvS7+406dOskVK1ZIKaVcunSp7NKli5RSykGDBsmePXtKnU4nvby8ZOXKlaWUUs6cOTPu7yk6Olq+fPkywc8yLCxMlilTRnp7e0sppRwwYICcPXt23N/x3LlzpZRSLliwQL7//vtJ4hk8eLDcsmWLlFLKRYsWxb1v5fUk+D+vGAU4I1P4vZqZXUOngSpCiIrAfaAPkLgO8Fa0lsByIUQRtK4i30yJJjoS9HrD1+j12nVplD9/fjw9PTl8+DD79++nd+/ezJgxg8GDBwPg7u4e9/WTTz4B4N69e/Tu3ZuHDx8SGRlJxYoV4+7XuXPnuJLMDRs25LvvvuPevXt0796dKlWqsG/fPjw9PalbV6tfHhYWRrFixZKNLbbWf3Bw4LwWVAAAHw5JREFUcNwYwZ49e9i2bVvchjexZaFBKy0dW6Wze/fuHDlyhK5du1K+fHkaNGgQ9/zYctagFbfz8fGhSZMmTJgwgUmTJtGpUyeaNGnC5cuXuXz5Mm3atAG0jXpi6yfFvgakXEoa4MWLFwwaNAgfHx+EEEZ9kj5+/DibN28GtPLZn332Wdy5rl27YmZmRo0aNeJaYnXr1mXo0KFERUXRtWvXuJLcsby9valYsSJVq1YFYNCgQSxYsICPP/44yfuIfd34hg0bxo8//kjXrl1Zvnw5S5YsSfU9KEpWybREIKWMFkJ8COxG6/9fJqX0EkJMR8tM22LOtRVCXAF0wEQp5bN0vfDoOckf37VEGxgOD0n+PIBNPnD/HDoMT/PLmpub07x5c5o3b46TkxMrV66MSwTxN7yJ/X7s2LGMHz+ezp07c+DAAb766qu4a+KXd+7bt2+SUs1SSgYNGsT336defGr16tW4ubkxceJExo4dy+bNm1MsC33y5Mkkm/PEPo4fkzRQEvvs2bPs2rWLqVOn0qpVK7p164ajoyPHjx9PNr7Y7rSUSkkDfPHFF7Ro0YItW7bg5+dH8+bNU33fhsQvuS1jJgg0bdqUQ4cOsXPnTgYPHsz48eOTFMEz5p4pvY/GjRvj5+fHgQMH0Ol0Ke79rCimkKljBFLKXVLKqlLKylLK72KOfRmTBIhpsYyXUtaQUjpJKdcZvmM6NOkJesP96Oh10LRXmm/t7e2dYJOY8+fPU758+bjHsX3l69evp2HDhoD2Kbd06dIArFy5MsV7J1equVWrVmzcuDGuhHRAQAC3b99O8R5CCL755htOnDjBtWvXUiwLDbB3714CAgIICwtj69atNG7cOMn9Uipn/eDBA+zs7Ojfvz8TJ07k7NmzVKtWjadPn8YlgqioKLy8DG8NmriMdfyf1YoVK1K8Lr5GjRrFbRm6evVqmjRpYvA1b9++TfHixRk+fDjDhg3j7NmzCc5Xq1YNPz+/uDGeP/74I00lsQEGDhxI3759VUlsJdsx9WBx1rF3gJ6fgrVd8uet7bTz+dO+gXxwcDCDBg2iRo0aODs7c+XKlQSf8AMDA3F2duaXX35h9uzZgDYo3KtXL9zc3JJsVxlfcqWaa9Sowbfffkvbtm1xdnamTZs2cQOwKbG1tWXChAn89NNPKZaFBm0PgB49euDs7EyPHj2oUydpscKUyllfunQpbgD766+/ZurUqVhZWbFx40YmTZqEi4sLrq6uHDtmeLpb4jLWn332GVOmTKFWrVoJPm0nvi6+efPmsXz58v9v78zDqyjSPfx+iUhYZFNAZBFQGYTsCEMcEhMggREUNHCRi5iAyCJBBgFh5qIyTpzRgSuM4IgbRpAJSBRccESWRIjKZROMyxgEEpAIssgSJCzJd//ok/YkOedkJyGp93n6Oaerq75aTp/+qqq7fo2/vz9Lly7lH//4h8c8U1JSCAgIICgoiBUrVhQRqvPx8eH1119n6NCh+Pn54eXlxfjx4z3aLMyIESP4+eef7alCg6G6UKtkqFG1HiFNmgte3taTQnXrWyOBIdMg5ilw897istK+fXu2b9/u8WJfXUhISCjwrmFDxZKUlMS7777L0qVLq7ooVzy1QYa6ovEkQ12r1EcRsdYJRD8Km5PgxGFodr01HVSGkYDBUFImTZrEv//97xItlDMYLje1a0RgMBhqBOY/X3rMi2kMBoPB4BbjCAwGg6GWYxyBwWAw1HKMIzAYDIZajnEEFcjq1asRkSKiY66YP38+v/zyqwDenXfeycmTpRe8K0z79u3x8/PDz8+PLl26MGvWLHJycopPWAIyMjLcrojNyspiyJAhQMllpEtC+/btiY6OtveTkpLsFdvuqMj8nUlJSWHgwIEVbtdgqGqMI6hAEhMT6dWrF4mJicXGLewIPvzwQ5o0qZhHWJOTk0lLS2Pr1q3s27fPpRRERXLp0iVuuOEGW8mzoi/EO3bs4Jtvvilx/MpwBO7kLwyGmoBxBBVEdnY2qampvPbaa7a0AVgia9OmTcPX1xd/f38WLFjA888/T1ZWFhEREURERABWz/fYsWPMnDmTF1749Y1Ds2fPtsXhXEk/e6Jhw4YsWrSI1atXc+LECbc2MjIyuPXWW3nooYfo2rUrUVFRnDt3DrAuwgEBAQQEBBQoV0JCAnfffTe9e/emT58+9mjBlTy0O9lrdxLbhZk6dSpPP/10kXBXdl3l7+fnx8mTJ1FVrr32WpYsWQJYkg/r1q0jJyeHUaNG4efnR1BQEMnJyS7r6My2bdsICgpi7969xf4OBkO1x50saXXdipWhriLefPNNHT16tKqqhoSE6Pbt21VV9Z///KdGR0fbMsrHjx9XVUu6+OjRo3b6/P2dO3dqWFiYHX7rrbfqgQMH3Eo/F6awXVXVgIAA3bJli1sb+/fvV29vb/3iiy9UVXXo0KG2TLWfn5+dz7Rp07Rr166qakkst27d2q7P/v37Cxxzlod2J3vtSmLbVX0OHz6snTt31j179ujKlSs1JibGo93C+Y8bN04/+OADTUtL09tuu82Wyr755ps1Oztb586da0tjf/vtt9q2bVs9d+5ckTomJyfrgAED9NNPP9Xg4GDNzMwsUl7D5aE6/OevNPAgQ21GBBVEYmIi9913HwD33XefPT20fv16xo0bZ79prFmzZh7tBAUF2QJuu3fvpmnTprRt27aA9HNwcDD/+c9/3PagC6OORYOebHTo0MGWXs6XhD558iQnT54kLCwMsOScnYmMjCy2Pvn5PvPMMwQGBhIeHm7LXoeEhPDXv/6VZ599lszMTFt6uzDe3t5Mnz69iNqqO7uFCQ0NZdOmTWzatIkJEyaQlpbGoUOHaNq0KQ0aNCA1NZX7778fgM6dO3PjjTeSnp7uso7ffvstY8eO5f333y/f2/IMhmpE7ZKYqCROnDjBxo0bSUtLQ0TIzc1FRJgzZ06Z7A0dOpSkpCQOHz7MsGHDAM/Sz544c+YMGRkZdOrUya2NjIyMAtLM3t7e9tSQJ5ylqT2hbmSvb7311iIS271793ZpY+TIkfztb38rcLPand3C74sOCwvjhRde4MCBAzz99NOsWrWKpKSkYhVJXdWxVatW5OTk8MUXX9hvoTMYrnTMiKACSEpKYuTIkWRmZpKRkcHBgwfp0KEDmzdvJjIykpdeesm+2Zg/V+9JQnnYsGEsX76cpKQkhg61ZLHdST97Ijs7m4cffpjBgwfTtGnTUtto0qQJTZo0ITU1FbDknEtC4bq5k712JbHtjjp16jBlyhRbvdWT3cL5t23blmPHjrFnzx46duxIr169mDt3rj3SCQ0NteuWnp7OgQMHijgX5zZZs2YNf/zjH0lJSSlRexgM1R3jCCqAxMRE7rnnngJh0dHRJCYmMmbMGNq1a4e/vz8BAQH861//AmDs2LH079/fvlnsTNeuXTlz5gytW7e23+blTvrZFREREfj6+tKjRw/atWvHSy+9VGob+bz++utMnDiRwMBA+4JbHIXlod3JXruS2PbEgw8+WODpHXd2XclT//a3v7XfLhYaGsqhQ4fo1asXAA8//DB5eXn4+fkxbNgwEhISCoyQCtOyZUs++OADJk6cWGT0YTBcidQ60bnrr5/LkSNF31LWsmUDDh+eViFlNBgMlYsRnSs9RnTOCVdOwFO4wWAw1HRq3M3iP/zhI3btOlymtOHhCS7DAwOvZ/78/uUolcFgMFRfat2IwGAwGAyFcLfAoLpu5V1QBrPdbuXhl19+0bCwML106ZIdNm/ePK1bt66ePHmy2PRPP/10gf2QkJBylScfLy8vDQgI0C5duqi/v7/OnTtXc3NzK8S2qurjjz+u69atU1WrvmfPnq0w2yXltddeU19fX/Xz89OuXbvq6tWrL3sZChMTE6Pt27fXgIAA9ff31/Xr11d1kUrE/v37ddmyZaWOt23bNp00aVKFlmXYsGGanp7u8phZUFZ68LCgrMov7KXdqqsjWLhwoc6fP79AWI8ePbRXr166ePHiYtM3aNCgXPmXxO6RI0e0T58++sQTT1SIbWenp+p6VXN5iYmJ0eTkZLfHDx48qB07drSd7ZkzZ3Tfvn3lzjd/JXhZiYmJ0ZUrV6qq6saNG/Xmm28ud5lUi7Z5RZO/erqi4pWHlJQUexV4YYwjKD2eHEGtmxpq2dL1Iih34SVl2bJlDBo0yN7fu3cv2dnZxMfHFxChy87OtnVt/P39efvtt5k5cybnzp0jMDCQESNGAJZOEFirlNesWWOnj42NJSkpidzcXKZPn27rBuU/IuqJFi1a8PLLL7Nw4UJU1a2NlJQUwsPDGTJkCJ07d2bEiBH2o6Pt27dnxowZBAcHs3LlSrs8rvSTPv74Y0JCQggODmbo0KH2+oWZM2fSpUsX/P39mTatfE9q/fTTT1xzzTV2ezVs2JAOHToAEB4ezuTJkwkMDMTX15etW7cCsHXrVkJCQggKCuL222/nu+++A4pqC/3444+EhYXZ6Tdv3uyxXu4ICQnh0KFDAB7bPCwsjAEDBvCb3/yG8ePHk5eXZ9dp6tSpBAQE8Pnnn/Pmm2/aGk3jxo0jNzeX3NxcYmNj8fX1xc/Pz15vsXfvXvr370+3bt0IDQ21lXFjY2N55JFHuP322+nYsaMtGDhz5kw2b95MYGAg8+bNIyMjg9DQUIKDgwkODuazzz5zGc9ZmfXEiRMMHjwYf39/evbsaa8PmT17NqNHjyY8PJyOHTvy/PPPA5Zm1IABAwgICMDX19d+5Dc0NJT169cbwb/LgTsPUV236qg1dP78eW3ZsmWBsPj4eH3qqac0NzdX27Vrp4cPH1ZV1ccee0wnT55sxztx4oSqFh0R5O+/8847+sADD9j5tGnTRn/55Rd96aWX9C9/+Yuqqubk5Gi3bt1c9oRdjTQaN26shw8fdmsjOTlZGzVqpAcPHtTc3Fzt2bOnbt68WVWtXv+zzz5r23Lu+TqPCI4ePaqhoaGanZ2tqqrPPPOM/vnPf9Zjx45pp06dNC8vT1UtjSBPFDciuHTpkkZFRWnbtm01NjZW33vvPfvYHXfcYfcoP/nkE1sL6dSpU3aPf926dXrvvfeqalH9pLlz52p8fLydz+nTp93Wy1W589tl1apVOnz4cFVVj21et25d3bt3r166dEn79u1rpwd0xYoVqmqd6wMHDtQLFy6oquqECRP0jTfe0O3bt2vfvn3t/PPbtXfv3vb0ypYtWzQiIsIu35AhQzQ3N1e//vprvemmm1S1aE//7Nmzeu7cOVVVTU9P1/z/X+F4zvtxcXE6e7Y1wt6wYYMGBASoquqTTz6pISEhmpOTo0ePHtVmzZrphQsXNCkpqUDP33kqtW/fvrZulzNV/Z+/EsHDiKDGPTVUFRw7dqyIhHRiYiKrVq3Cy8uL6OhoVq5cSVxcHOvXry+gTtq0aVOPtn//+98zefJkzp8/z0cffURYWBj16tXj448/5ssvv7R7cqdOnWLPnj12b7gkuLNx9dVX06NHD9q0aQNAYGAgGRkZ9gKsfNkLT2zZsoVvvvmG3/3udwBcuHCBkJAQGjdujI+PDw8++CADBw50qe+/du1aZsyYAcCBAwdITU2lYcOG1K1bt8gCLm9vbz766CO2bdvGhg0bmDJlCjt27GD27NkADB8+HLBkJk6fPs3Jkyc5c+YMMTEx7NmzBxHh4sWLtj1nbaHu3bszevRoLl68yODBgwkMDOSTTz5xWS9XTJ8+nT/96U/88MMPfP755yVq844dO9rlTk1NZciQIXh7e9vvZNiwYQM7duyge/fuAJw7d44WLVpw1113sW/fPiZNmsSAAQOIiooiOzubzz77zF6dDnD+/Hn7++DBg/Hy8qJLly4cOXLEZR0uXrxIXFwcu3btwtvb29Zg8kRqaipvv/02AL179+b48eOcPn0agAEDBlC3bl3q1q1LixYtOHLkCH5+fkydOpUZM2YwcODAAtIfLVq0ICsri27duhWbr6HsGEdQAdSrV6/Ay1/S0tLYs2cPkZGRgHWx6NChA3FxcaW27ePjQ3h4OGvXrmXFihW2sJ2qsmDBAvr161cqe/v27cPb25sWLVq4tZGSklJEe8h5eF4SjSFVJTIy0uW7GbZu3cqGDRtISkpi4cKFbNy4scDxfv362WWKjY0lNjaW8PBwt3mJCD169KBHjx5ERkYyatQo2xGISJG4jz/+OBEREaxatYqMjIwCtp3rFhYWxqZNm1izZg2xsbE8+uijNG3a1G29CjNnzhyGDBnCggULGD16NDt27PDY5q7KCtY54O3tDVjtGhMTU0SAD2D37t2sXbuWRYsW8dZbbzF//nyaNGnCrl27XJbP+TdWdb2wdN68ebRs2ZLdu3eTl5eHj49PsfX2hKvzqlOnTuzcuZMPP/yQWbNm0adPH5544gkAcnJy3IoRGiqOWnePoDJo2rQpubm5tjNITExk9uzZZGRkkJGRQVZWFllZWWRmZhIZGVlA1//nn38GLC0d556pM8OGDeP1119n8+bN9O9vrWfo168fL774op0mPT2ds2c9L4o7evQo48ePJy4uDhEpkw1POGv89OzZk08//ZTvv/8esOaB09PTyc7O5tSpU9x5553MmzeP3bt3lzk/sN6MtnPnTnt/165d3HjjjfZ+/nxzamoqjRs3pnHjxpw6dYrWrVsD1n0Bd2RmZtKyZUseeughxowZw86dO93WyxNxcXHk5eWxdu1aj22+detW9u/fT15eHitWrLBHYM706dOHpKQkWyPqxIkTZGZmcuzYMfLy8oiOjiY+Pp6dO3fSqFEjOnTowMqVKwHrYl9cexfWaTp16hStWrXCy8uLpUuXkpub6zKeM87aTSkpKVx33XU0atTIbZ5ZWVnUr1+f+++/n+nTpxf4PdPT092+Fc9QcZgRQQURFRVFamoqffv2Zfny5UXekHXPPfewfPlyZs2axcSJE/H19cXb25snn3ySe++9l7Fjx+Lv709wcHARcbeoqChGjhzJoEGDuPrqqwEYM2YMGRkZBAcHo6o0b96c1atXFylX/k3oixcvctVVVzFy5EgeffTRUtkoKfn6STfccAPJyckkJCQwfPhwezoiPj6ea665hkGDBpGTk4Oq8txzz5U5P7CmLqZNm0ZWVhY+Pj40b96cRYsW2cd9fHwICgri4sWLLF68GIDHHnuMmJgY4uPjGTBggFvbKSkpzJkzhzp16tCwYUOWLFlC8+bNXdYrX8fIFSLCrFmz+Pvf/866devctnn37t2Ji4vj+++/JyIiooh+FUCXLl2Ij48nKiqKvLw86tSpwwsvvEC9evUYNWqUfYM5f8SwbNkyJkyYQHx8PBcvXuS+++4jICDAbVn9/f3x9vYmICCA2NhYHn74YaKjo1myZAn9+/e3R0yF4wUFBdk28m8K+/v7U79+fd544w23+YE1gp4+fTpeXl7UqVOHF198EYAjR45Qr149rr/+eo/pDRWAu5sH1XWrjjeLVVV37Nih999/f1UXw+DEHXfcodu2bavqYpSIy/E45pXGc889p6+++qrLY9XhP3+lgXl8tPIJDg4mIiLCHjobDIby0aRJE2JiYqq6GLWCWqc+ajAYrnzMf7701Ar10SvNoRkMhrJh/usVT41wBD4+Phw/ftycIAZDDUdVOX78eLkfYzUUpEY8NdSmTRt++OEHjh49WtVFMRgMlYyPj4+92NFQMdQIR1CnTp1Srag1GAwGw69U6tSQiPQXke9E5HsRmenieKyIHBWRXY5tTGWWx2AwGAxFqbQRgYh4Ay8AkcAPwDYReU9VvykUdYWqll57wWAwGAwVQmWOCHoA36vqPlW9ACwHBhWTxmAwGAyXmcq8R9AaOOi0/wPwWxfxokUkDEgHpqjqwcIRRGQsMNaxmy0i35WxTNcBx8qYtiJoDJyqxnbL2j6lzb+k8UsSryRxqvp3rwyutN/8cts153JRbnR7xN2S4/JuwBDgVaf9kcDCQnGuBeo6vo8DNlZWeRx5uF1ifTk24OXqbLes7VPa/EsavyTxShinSn9385vX3HrVlHO5MqeGDgFtnfbbOMJsVPW4quYLpL8K1HTR8fevMLuVlX9J45ckXlXXvaqo6nqbc7l08av1uVxpEhMichXWdE8fLAewDfhvVf3aKU4rVf3R8f0eYIaq9qyUAll5bFc3S6wNNbd9amq9KoKa2jamXqWj0u4RqOolEYkD1gLewGJV/VpEnsIa3rwHPCIidwOXgBNAbGWVx8HLlWz/Sqemtk9NrVdFUFPbxtSrFFxxonMGg8FgqFhqhNaQwWAwGMqOcQQGg8FQyzGOwFBjEJHFIvKTiHzlFNZMRNaJyB7HZ9OqLKPB4I7SnL9i8bxDvudLEQkuT97GERhqEglA/0JhM4ENqnoLsMGxbzBURxIo+fn7e+AWxzYWeLE8GRtHYKgxqOomrKfPnBkE5L89/Q1g8GUtlMFQQkp5/g4ClqjFFqCJiLQqa97GERhqOi3z16oAh4GWVVkYg6GUuDt/XUn4tC5rJsYRGGoNaj0rbZ6XNlyRVOb5axyBoaZzJH/I7Pj8qYrLYzCUBnfnb7ESPqXBOAJDTec9IMbxPQZ4twrLYjCUFnfn73vAA46nh3oCp5ymkEqNWVlsqDGISCIQjiXVewR4ElgNvAW0AzKB/1LVwjfkDIYqpzTnr4gIsBDrKaNfgFGqur3MeRtHYDAYDLUbMzVkMBgMtRzjCAwGg6GWYxyBwWAw1HKMIzAYDIZajnEEBoPBUMsxjsBQLkREReR/nfanicjsCrKdICJDKsJWMfkMFZFvRSS5UHh7EfnvctjNLkfaJBHp6PieISLXldVWRVKS30RE5opI78tVJkP5MY7AUF7OA/dWlwtVPo53ZpeUB4GHVDWiUHh7oMyOoKyISFfAW1X3Xe68K4gFGJXXKwrjCAzl5RLWe1SnFD5QuPeY30MWkXAR+URE3hWRfSLyjIiMEJGtIpImIjc5mekrIttFJF1EBjrSe4vIHBHZ5tBiH+dkd7OIvAd846I8wx32vxKRZx1hTwC9gNdEZE6hJM8AoSKyS0SmOEYIm0Vkp2O73WGjlYhscsT7SkRCC+V7nYh8LiIDiovrYARuVkCLyKOOdF+JyB+cwh8Xke9EJFVEEkVkmou0Qx3pdovIJqe2nOsI/1JEJuW3i6N9vxKRlx0LmArb6+b4HXeIyNp8KQRVzQSuFZHrXdXBUA1RVbOZrcwbkA00AjKAxsA0YLbjWAIwxDmu4zMcOAm0AupiaaT82XFsMjDfKf1HWB2WW7AUFn2w9NdnOeLUBbYDHRx2zwIdXJTzBuAA0By4CtgIDHYcSwFuc5EmHPjAab8+4OP4fguw3fF9KvA/ju/ewDVObdMS+D8g0lPcQvl+Avg57WdgrTbtBqQBDYCGwNdAENAd2OVom2uAPcA0F3bTgNaO700cnxOAJOAqx34z50/H96XAXc6/KVAH+Axo7ggfBix2SvMKEF3V56fZSraVZvhsMLhEVU+LyBLgEeBcCZNtU4c2iojsBT52hKcBzlM0b6lqHrBHRPYBnYEowN9ptNEY68J8Adiqqvtd5NcdSFHVo448lwFhWEv4S0odYKGIBAK5QKf8ugCLRaQOsFpVdznF3wBMVNVPionrTCvgqIvwXsAqVT3rqMM7QCiWo3xXVXOAHBF53035PwUSROQt4B1HWF9gkapeAtBf5TciROQxLOfXDMvpONv9DeALrHMMFrwBZ62bn7Ccr+EKwEwNGSqK+Vhz7Q2cwi7hOMdExAu42unYeafveU77eVCgg1JYA0UBASapaqBj66Cq+Y7kbLlq4ZkpWBowAcBtOOqj1gtFwrBGNgki8oAj/iVgB9DPLrz7uM6cw+rdVyiqOh6YhaVauUNErnUVT0R8gH9ijeb8sHr3hcsjwNdOv4GfqkY5Hfeh5J0CQxVjHIGhQnD0JN/Ccgb5ZGBNZwDcjdVDLi1DRcTLcd+gI/AdsBaY4OhVIyKdRKSBJyPAVuAOx3y9NzAcawrGE2ewplryaQz86BihjMTqBSMiNwJHVPUV4FUg//2xCowGOovIjGLiOvMtcLOL8M3AYBGp76jvPY6wT4G7RMRHRBoCA11VRkRuUtX/U9UnsEYcbYF1wLj8m+si0oxfL/rHHPZcPSX0HdBcREIc6eo4bnLn0wn4ykU6QzXETA0ZKpL/BeKc9l8B3hWR3Vhz/WXprR/Auog3Asarao6IvIr1RM9Ox03MoxTzCkpV/VFEZgLJWL3ZNapanCT1l0Cuo/wJWL3ktx29eOf6hAPTReQi1n0Bu5evqrkiMhx4T0TOONK4jOvEGofN9YXqsFNEEhztAfCqqn4B4LhB/iXWiCUNOOXC7hwRucVR/w3AbqyLdSfgS0eZXlHVhSLyiuPYYazprAKo6gXH1NzzItIY61oyH/ja4aBvxrp3Y7gCMOqjBkM1Q0TqYTms36lqbgnTNFTVbBGpD2wCxqrqzsosp4ey3AMEq+rjVZG/ofSYEYHBUM1Q1XMi8iTWO2gPlDDZyyLSBWta542qcgIOrsIaHRquEMyIwGAwGGo55maxwWAw1HKMIzAYDIZajnEEBoPBUMsxjsBgMBhqOcYRGAwGQy3n/wFxKUcZNRSqHAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "rQzq4osdhPpP",
        "outputId": "181e8029-56ac-41c6-e35d-951b587f69ee"
      },
      "source": [
        "\"\"\"\n",
        "The number of clusters formed when inferring prototype context vectors while training\n",
        "via a clustering approach. All numbers are the average number of prototypes found over 8\n",
        "independent trials.\n",
        "\"\"\"\n",
        "\n",
        "num_clusters_found = [2.25, 5.625, 11.375, 28.125, 56.75, 110.5]\n",
        "\n",
        "fig, ax = plt.subplots()\n",
        "ax.plot(num_tasks, num_tasks, \"--\", color=\"k\", label=\"Num. clusters = Num. tasks\")\n",
        "ax.plot(num_tasks, num_clusters_found, \"-o\", label=\"Prototype inferred while training\")\n",
        "\n",
        "ax.set_ylabel(\"Number of clusters\")\n",
        "ax.set_xlabel(\"Number of tasks (log scale)\")\n",
        "ax.set_xscale(\"log\")\n",
        "ax.set_xticks(num_tasks)\n",
        "ax.set_xticklabels(x_labels)\n",
        "ax.legend(loc=\"upper left\")"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f7b23654210>"
            ]
          },
          "metadata": {},
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}